Genomics & Informatics

Korea Genome Organization (한국유전체학회)
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Effect of Normalization on Detection of Differentially-Expressed Genes with Moderate Effects

  • Cho, Seo-Ae (Interdisciplinary Program in Bioinformatics, Seoul National University) ;
  • Lee, Eun-Jee (Interdisciplinary Program in Bioinformatics, Seoul National University) ;
  • Kim, Young-Chul (Department of Statistics, Seoul National University) ;
  • Park, Tae-Sung (Department of Statistics, Seoul National University)
  • Published : 2007.09.30
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Abstract

The current existing literature offers little guidance on how to decide which method to use to analyze one-channel microarray measurements when dealing with large, grouped samples. Most previous methods have focused on two-channel data;therefore they can not be easily applied to one-channel microarray data. Thus, a more reliable method is required to determine an appropriate combination of individual basic processing steps for a given dataset in order to improve the validity of one-channel expression data analysis. We address key issues in evaluating the effectiveness of basic statistical processing steps of microarray data that can affect the final outcome of gene expression analysis without focusingon the intrinsic data underlying biological interpretation.

Keywords

References

  1. Bolstad, B.M., Irizarry, RA, Astrand, M. and Speed, 1.P. (2003). A comparison of normaliza-tion methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185-193 https://doi.org/10.1093/bioinformatics/19.2.185
  2. Edwards, D. (2003). Non-linear normalization and background correction in one-channel cDNA microarray studies. Bioinformatics 19, 825-833 https://doi.org/10.1093/bioinformatics/btg083
  3. Futschik, M. and Crompton, T. (2004). Model selection and efficiency testing for normalization of cDNA microarray data. Genome Biol. 5, R60 https://doi.org/10.1186/gb-2004-5-8-r60
  4. Cui, X. and Churchill, G. A (2003). Statistical tests for differential expression in cDNA microarray experiments. Genome BioI. 4,210 https://doi.org/10.1186/gb-2003-4-4-210
  5. Smyth, G. K. and Speed, T. (2003). Normalization of cDNA microarray data. Methods 31,265-273 https://doi.org/10.1016/S1046-2023(03)00155-5
  6. Toni W., and Elizabeth R.U. Intergration of gene expression, clinical and epidemiologic data to characterize chronic Fatigue Syndrome. Journal of Translational Medicine
  7. Park,T., Vi, S.G., Lee, S.Y. and Lee, J.K. (2005). Diagnostic plots for detecting outlying slides in a cDNA microarray experiment. Bio. Techniques 38, 463-471 https://doi.org/10.2144/05383RR02
  8. Yang, Y. H., Dudoit, S., Luu, P., Lin, D. M., Peng, V. , Ngai, J. and Speed, T. P. (2002). Normal-ization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res. 30, e15 https://doi.org/10.1093/nar/30.4.e15
  9. Irizarry, R. A., Hobbs, B., Collin, F., Beazer-Barclay, Y. D., Antonellis, K. J., Scherf, U. and Speed, T. P. (2003). Exploration, normalization and summaries of high density oligonucleotide array probe level data. Biostatistics 4, 249-264 https://doi.org/10.1093/biostatistics/4.2.249
  10. Li, C. and Wong, W.H.(2001). Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proceedings ofthe NationalAcademy of Sciences 98,31-36